Electromagnetic Biology and Medicine最新文献

The brain is a crucial organ that controls the body's neural system. The tumor develops and spreads across the brain as a result of irregular cell generation. The provision of substantial treatment to patients requires the early diagnosis of malignancies. However, timely diagnosis and accurate classification were difficult in the conventional models. Thus, the Taylor Fire Hawk optimization (TFHO) is implemented here for effective segmentation and classification. The TFHO is the merging of the Taylor series and Fire Hawk Optimizer (FHO). The de-noising is accomplished by the adaptive median filter, and the segmentation is carried out using M-Net, which has been trained by TFHO. Subsequently, image augmentation is performed to increase the image dimension, followed by the extraction of effective features. Finally, DenseNet is used for the classification, and the training is done by TFHO. The introduced method obtained 94.86% accuracy, 92.83% Negative Predictive Values, 89.33% Positive Predictive Values (PPV), 95.91% True Positive Rate (TPR), 4.37% False Negative Rate (FNR), and 90.98% F1-score.

Brain tumors present a formidable diagnostic challenge due to their aberrant cell growth. Accurate determination of tumor location and size is paramount for effective diagnosis. Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) are pivotal tools in clinical diagnosis, yet tumor segmentation within their images remains challenging, particularly at boundary pixels, owing to limited sensitivity. Recent endeavors have introduced fusion-based strategies to refine segmentation accuracy, yet these methods often prove inadequate. In response, we introduce the Parallel-Way framework to surmount these obstacles. Our approach integrates MRI and PET data for a holistic analysis. Initially, we enhance image quality by employing noise reduction, bias field correction, and adaptive thresholding, leveraging Improved Kalman Filter (IKF), Expectation Maximization (EM), and Improved Vibe Algorithm (IVib), respectively. Subsequently, we conduct multi-modality image fusion through the Dual-Tree Complex Wavelet Transform (DTWCT) to amalgamate data from both modalities. Following fusion, we extract pertinent features using the Advanced Capsule Network (ACN) and reduce feature dimensionality via Multi-objective Diverse Evolution-based selection. Tumor segmentation is then executed utilizing the Twin Vision Transformer with dual attention mechanism. Implemented our Parallel-Way framework which exhibits heightened model performance. Evaluation across multiple metrics, including accuracy, sensitivity, specificity, F1-Score, and AUC, underscores its superiority over existing methodologies.

Results from clinical trials show that serotonergic psychedelics have efficacy in treating psychiatric disorders, where currently approved pharmacotherapies are inadequate. Developing psychedelic medicines, however, comes with unique challenges, such as tempering heightened anxiety associated with the psychedelic experience. We conceived a new strategy to potentially mitigate psychedelic effects with defined electromagnetic signals (ES). We recorded the electromagnetic fields emitted by the serotonin 2 receptor (5-HT₂R) agonist (±)-2,5-dimethoxy-4-iodoamphetamine (DOI) and converted them to a playable WAV file. We then exposed the DOI WAV ES to mice to assess its effects on the DOI-elicited, 5-HT_2AR dependent head-twitch response (HTR). The DOI WAV signal significantly attenuated the HTR in mice elicited by 0.1 and 0.3 mg/kg subcutaneous DOI (p < 0.05 and p < 0.01, respectively). A scrambled WAV signal did not affect the DOI-elicited HTR, suggesting specificity of the DOI WAV signal. These results provide evidence that defined ES could modulate the psychoactive effects of serotonergic psychedelics. We discuss putative explanations for the distinct effects of the DOI WAV signal in the context of previous studies that demonstrate ES's efficacy for treating other conditions, including pain and cancer.

Human being's biological processes and psychological activities are jointly connected to the brain. So, the examination of human activity is more significant for the well-being of humans. There are various models for brain activity detection considering neuroimaging for attaining decreased time requirement, increased control commands, and enhanced accuracy. Motor Imagery (MI)-based Brain-Computer Interface (BCI) systems create a way in which the brain can interact with the environment by processing Electroencephalogram (EEG) signals. Human Activity Recognition (HAR) deals with identifying the physiological activities of human beings based on sensory signals. This survey reviews the different methods available for HAR based on MI-EEG signals. A total of 50 research articles based on HAR from EEG signals are considered in this survey. This survey discusses the challenges faced by various techniques for HAR. Moreover, the papers are assessed considering various parameters, techniques, publication year, performance metrics, utilized tools, employed databases, etc. There were many techniques developed to solve the problem of HAR and they are classified as Machine Learning (ML) and Deep Learning (DL)models. At last, the research gaps and limitations of the techniques were discussed that contribute to developing an effective HAR.

The electromagnetic waves of 2.45 GHz microwave frequency have become abundant in environments worldwide. This study assessed the short-term impact of low-intensity 2.45 GHz exposure on young Arabidopsis thaliana plants. The plants underwent a 48-hour exposure to continuous wave 2.45 GHz microwaves at a power density of 1.0 ± 0.1 W m^-2. Experiments were conducted inside anechoic chambers. After the microwave exposure samples were subjected to morphological, genotoxicity, pigmentation, and physiochemical analysis. Microwave exposure elevated the levels of photosynthetic pigments, oxidative stress, guaiacol peroxidase activity, and ascorbic peroxidase activity in plants. Conversely, catalase activity decreased. Photosystem efficiency remained unchanged, while non-photochemical quenching increased. Leaf morphological parameters exhibited no significant alterations during this brief exposure period. Notably, despite shifts in physiological parameters and pigmentations, genomic template stability remained unaffected. The findings suggest that the non-thermal effects of microwave exposure influence the photosystem and plant physiology. Research confirmed the existence of non-thermal effects of microwave exposure; however, these effects are within tolerable limits for Arabidopsis thaliana plants.

In this study, we investigated the inhibitory effects of radiofrequency exposure on RANKL-induced osteoclast differentiation in RAW264.7 cells, along with the underlying mechanisms. RAW264.7 cells were subjected to radiofrequency exposure at three distinct power densities: 50 µW/cm², 150 µW/cm², and 450 µW/cm². The results showed that, among the three dosage levels, exposure to 150 µW/cm² of radiofrequency radiation significantly reduced the proliferation capacity of RAW264.7 cells. RF exposure at three power densities resulted in significant increases in the level of osteoclast apoptosis and notable decreases in osteoclast differentiation. Notably, the most pronounced effects on apoptosis, differentiation in RAW 264.7 cells were observed at the 150 µW/cm² power density. These effects were accompanied by concurrent decreases in mRNA and protein levels of osteoclast-specific genes, including RANK, NFATc1, and TRACP. Furthermore, radiofrequency exposure at power density of 150 µW/cm² induced a significant decrease in cytoplasmic NF-κB protein levels while increasing its nuclear fraction, thereby counteracting the effects of RANKL-induced NF-κB activation. These data suggest that radiofrequency exerts inhibitory properties on RANKL-induced NF-κB transcriptional activity, subsequently indirectly suppressing the expression of downstream NF-κB target genes, such as NFATc1 and TRACP. In conclusion, our study demonstrates that radiofrequency radiation effectively inhibits osteoclast differentiation by modulating the NF-κB signaling pathway. These findings have important implications for potential therapeutic interventions in osteoporosis.

Efficient and accurate classification of brain tumor categories remains a critical challenge in medical imaging. While existing techniques have made strides, their reliance on generic features often leads to suboptimal results. To overcome these issues, Multimodal Contrastive Domain Sharing Generative Adversarial Network for Improved Brain Tumor Classification Based on Efficient Invariant Feature Centric Growth Analysis (MCDS-GNN-IBTC-CGA) is proposed in this manuscript.Here, the input imagesare amassed from brain tumor dataset. Then the input images are preprocesssed using Range - Doppler Matched Filter (RDMF) for improving the quality of the image. Then Ternary Pattern and Discrete Wavelet Transforms (TPDWT) is employed for feature extraction and focusing on white, gray mass, edge correlation, and depth features. The proposed method leverages Multimodal Contrastive Domain Sharing Generative Adversarial Network (MCDS-GNN) to categorize brain tumor images into Glioma, Meningioma, and Pituitary tumors. Finally, Coati Optimization Algorithm (COA) optimizes MCDS-GNN's weight parameters. The proposed MCDS-GNN-IBTC-CGA is empirically evaluated utilizing accuracy, specificity, sensitivity, Precision, F1-score,Mean Square Error (MSE). Here, MCDS-GNN-IBTC-CGA attains 12.75%, 11.39%, 13.35%, 11.42% and 12.98% greater accuracy comparing to the existingstate-of-the-arts techniques, likeMRI brain tumor categorization utilizing parallel deep convolutional neural networks (PDCNN-BTC), attention-guided convolutional neural network for the categorization of braintumor (AGCNN-BTC), intelligent driven deep residual learning method for the categorization of braintumor (DCRN-BTC),fully convolutional neural networks method for the classification of braintumor (FCNN-BTC), Convolutional Neural Network and Multi-Layer Perceptron based brain tumor classification (CNN-MLP-BTC) respectively.

Anxiety is an adaptive condition characterized by heightened uneasiness, which in the long term can cause complications such as reducing the quality of life and problems related to the mental and physical health. Concerns have been raised regarding the potential dangers of extremely low frequency electromagnetic fields (ELF-EMF) ranging from 3 to 3000 Hz, which are omnipresent in our daily lives and there have been studies about the anxiogenic effects of these fields. Studies conducted in this specific area has revealed that ELF-EMF can have an impact on various brain regions, such as the hippocampus. In conclusion, studies have shown that ELF-EMF can interfere with hippocampus-prefrontal cortex pathway, inducing anxiety behavior. Also, ELF-EMF may initiate anxiety behavior by generating oxidative stress in hypothalamus and hippocampus. Moreover, ELF-EMF may induce anxiety behavior by reducing hippocampus neuroplasticity and increasing the NMDA2_A receptor expression in the hippocampus. Furthermore, supplementation with antioxidants could serve as an effective protective measure against the adverse effects of FLF-FMF in relation to anxiety behavior.

The effect of non-ionizing millimeter range electromagnetic waves (MM EMW) (30-300 GHz) on the bovine serum albumin (BSA) interaction peculiarities with acridine orange (AO) has been studied in vitro. The frequencies 41.8 and 50.3 GHz were chosen, since the first one is nonresonant frequency for the water, while the second one is resonant for water. The binding constant and number of binding sites were calculated at both irradiation presence and absence. AO was revealed to bind to BSA, while after the protein irradiation the interaction force strengthens. However, it was also shown that there are differences of the interaction parameters while irradiating by 41.8 or 50.3 GHz. AO binds to BSA, irradiated by MM EMW with the frequency 41.8 GHz much more weaker, than to that, irradiated by MM EMW with the frequency 50.3 GHz.